Pattern memory analysis based on stability theory of cellular neural networks

AbstractIn this paper, several sufficient conditions are obtained to guarantee that the n-dimensional cellular neural network can have even (⩽2n) memory patterns. In addition, the estimations of attractive domain of such stable memory patterns are obtained. These conditions, which can be directly derived from the parameters of the neural networks, are easily verified. A new design procedure for cellular neural networks is developed based on stability theory (rather than the well-known perceptron training algorithm), and the convergence in the new design procedure is guaranteed by the obtained local stability theorems. Finally, the validity and performance of the obtained results are illustrated by two examples

Image-based quantitative analysis of gold immunochromatographic strip via cellular neural network approach

"(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works."Gold immunochromatographic strip assay provides a rapid, simple, single-copy and on-site way to detect the presence or absence of the target analyte. This paper aims to develop a method for accurately segmenting the test line and control line of the gold immunochromatographic strip (GICS) image for quantitatively determining the trace concentrations in the specimen, which can lead to more functional information than the traditional qualitative or semi-quantitative strip assay. The canny operator as well as the mathematical morphology method is used to detect and extract the GICS reading-window. Then, the test line and control line of the GICS reading-window are segmented by the cellular neural network (CNN) algorithm, where the template parameters of the CNN are designed by the switching particle swarm optimization (SPSO) algorithm for improving the performance of the CNN. It is shown that the SPSO-based CNN offers a robust method for accurately segmenting the test and control lines, and therefore serves as a novel image methodology for the interpretation of GICS. Furthermore, quantitative comparison is carried out among four algorithms in terms of the peak signal-to-noise ratio. It is concluded that the proposed CNN algorithm gives higher accuracy and the CNN is capable of parallelism and analog very-large-scale integration implementation within a remarkably efficient time

Convergence of Discrete-Time Cellular Neural Networks with Application to Image Processing

The paper considers a class of discrete-time cellular neural networks (DT-CNNs) obtained by applying Euler's discretization scheme to standard CNNs. Let T be the DT-CNN interconnection matrix which is defined by the feedback cloning template. The paper shows that a DT-CNN is convergent, i.e. each solution tends to an equilibrium point, when T is symmetric and, in the case where T + En is not positive-semidefinite, the step size of Euler's discretization scheme does not exceed a given bound (En is the n × n unit matrix). It is shown that two relevant properties hold as a consequence of the local and space-invariant interconnecting structure of a DT-CNN, namely: (1) the bound on the step size can be easily estimated via the elements of the DT-CNN feedback cloning template only; (2) the bound is independent of the DT-CNN dimension. These two properties make DT-CNNs very effective in view of computer simulations and for the practical applications to high-dimensional processing tasks. The obtained results are proved via Lyapunov approach and LaSalle's Invariance Principle in combination with some fundamental inequalities enjoyed by the projection operator on a convex set. The results are compared with previous ones in the literature on the convergence of DT-CNNs and also with those obtained for different neural network models as the Brain-State-in-a-Box model. Finally, the results on convergence are illustrated via the application to some relevant 2D and 1D DT-CNNs for image processing tasks

Novel arithmetic implementations using cellular neural network arrays.

The primary goal of this research is to explore the use of arrays of analog self-synchronized cells---the cellular neural network (CNN) paradigm---in the implementation of novel digital arithmetic architectures. In exploring this paradigm we also discover that the implementation of these CNN arrays produces very low system noise; that is, noise generated by the rapid switching of current through power supply die connections---so called di/dt noise. With the migration to sub 100 nanometer process technology, signal integrity is becoming a critical issue when integrating analog and digital components onto the same chip, and so the CNN architectural paradigm offers a potential solution to this problem. A typical example is the replacement of conventional digital circuitry adjacent to sensitive bio-sensors in a SoC Bio-Platform. The focus of this research is therefore to discover novel approaches to building low-noise digital arithmetic circuits using analog cellular neural networks, essentially implementing asynchronous digital logic but with the same circuit components as used in analog circuit design. We address our exploration by first improving upon previous research into CNN binary arithmetic arrays. The second phase of our research introduces a logical extension of the binary arithmetic method to implement binary signed-digit (BSD) arithmetic. To this end, a new class of CNNs that has three stable states is introduced, and is used to implement arithmetic circuits that use binary inputs and outputs but internally uses the BSD number representation. Finally, we develop CNN arrays for a 2-dimensional number representation (the Double-base Number System - DBNS). A novel adder architecture is described in detail, that performs the addition as well as reducing the representation for further processing; the design incorporates an innovative self-programmable array. Extensive simulations have shown that our new architectures can reduce system noise by almost 70dB and crosstalk by more than 23dB over standard digital implementations.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .I27. Source: Dissertation Abstracts International, Volume: 66-11, Section: B, page: 6159. Thesis (Ph.D.)--University of Windsor (Canada), 2005

Recent Advances and Applications of Fractional-Order Neural Networks

This paper focuses on the growth, development, and future of various forms of fractional-order neural networks. Multiple advances in structure, learning algorithms, and methods have been critically investigated and summarized. This also includes the recent trends in the dynamics of various fractional-order neural networks. The multiple forms of fractional-order neural networks considered in this study are Hopfield, cellular, memristive, complex, and quaternion-valued based networks. Further, the application of fractional-order neural networks in various computational fields such as system identification, control, optimization, and stability have been critically analyzed and discussed

Aspects of algorithms and dynamics of cellular paradigms

Els paradigmes cel·lulars, com les xarxes neuronals cel·lulars (CNN, en anglès) i els autòmats cel·lulars (CA, en anglès), són una eina excel·lent de càlcul, al ser equivalents a una màquina universal de Turing. La introducció de la màquina universal CNN (CNN-UM, en anglès) ha permès desenvolupar hardware, el nucli computacional del qual funciona segons la filosofia cel·lular; aquest hardware ha trobat aplicació en diversos camps al llarg de la darrera dècada. Malgrat això, encara hi ha moltes preguntes a obertes sobre com definir els algoritmes d'una CNN-UM i com estudiar la dinàmica dels autòmats cel·lulars. En aquesta tesis es tracten els dos problemes: primer, es demostra que es possible acotar l'espai dels algoritmes per a la CNN-UM i explorar-lo gràcies a les tècniques genètiques; i segon, s'expliquen els fonaments de l'estudi dels CA per mitjà de la dinàmica no lineal (segons la definició de Chua) i s'il·lustra com aquesta tècnica ha permès trobar resultats innovadors.Los paradigmas celulares, como las redes neuronales celulares (CNN, eninglés) y los autómatas celulares (CA, en inglés), son una excelenteherramienta de cálculo, al ser equivalentes a una maquina universal deTuring. La introducción de la maquina universal CNN (CNN-UM, eninglés) ha permitido desarrollar hardware cuyo núcleo computacionalfunciona según la filosofía celular; dicho hardware ha encontradoaplicación en varios campos a lo largo de la ultima década. Sinembargo, hay aun muchas preguntas abiertas sobre como definir losalgoritmos de una CNN-UM y como estudiar la dinámica de los autómatascelular. En esta tesis se tratan ambos problemas: primero se demuestraque es posible acotar el espacio de los algoritmos para la CNN-UM yexplorarlo gracias a técnicas genéticas; segundo, se explican losfundamentos del estudio de los CA por medio de la dinámica no lineal(según la definición de Chua) y se ilustra como esta técnica hapermitido encontrar resultados novedosos.Cellular paradigms, like Cellular Neural Networks (CNNs) and Cellular Automata (CA) are an excellent tool to perform computation, since they are equivalent to a Universal Turing machine. The introduction of the Cellular Neural Network - Universal Machine (CNN-UM) allowed us to develop hardware whose computational core works according to the principles of cellular paradigms; such a hardware has found application in a number of fields throughout the last decade. Nevertheless, there are still many open questions about how to define algorithms for a CNN-UM, and how to study the dynamics of Cellular Automata. In this dissertation both problems are tackled: first, we prove that it is possible to bound the space of all algorithms of CNN-UM and explore it through genetic techniques; second, we explain the fundamentals of the nonlinear perspective of CA (according to Chua's definition), and we illustrate how this technique has allowed us to find novel results

Downregulation of genes with a function in axon outgrowth and synapse formation in motor neurones of the VEGF(delta/delta) mouse model of amyotrophic lateral sclerosis

Background: Vascular endothelial growth factor (VEGF) is an endothelial cell mitogen that stimulates vasculogenesis. It has also been shown to act as a neurotrophic factor in vitro and in vivo. Deletion of the hypoxia response element of the promoter region of the gene encoding VEGF in mice causes a reduction in neural VEGF expression, and results in adult-onset motor neurone degeneration that resembles amyotrophic lateral sclerosis (ALS). Investigating the molecular pathways to neurodegeneration in the VEGF(delta/delta) mouse model of ALS may improve understanding of the mechanisms of motor neurone death in the human disease. Results: Microarray analysis was used to determine the transcriptional profile of laser captured spinal motor neurones of transgenic and wild-type littermates at 3 time points of disease. 324 genes were significantly differentially expressed in motor neurones of presymptomatic VEGF(delta/delta) mice, 382 at disease onset, and 689 at late stage disease. Massive transcriptional downregulation occurred with disease progression, associated with downregulation of genes involved in RNA processing at late stage disease. VEGF(delta/delta) mice showed reduction in expression, from symptom onset, of the cholesterol synthesis pathway, and genes involved in nervous system development, including axonogenesis, synapse formation, growth factor signalling pathways, cell adhesion and microtubule-based processes. These changes may reflect a reduced capacity of VEGF(delta/delta) mice for maintenance and remodelling of neuronal processes in the face of demands of neural plasticity. The findings are supported by the demonstration that in primary motor neurone cultures from VEGF(delta/delta) mice, axon outgrowth is significantly reduced compared to wild-type littermates. Conclusions: Downregulation of these genes involved in axon outgrowth and synapse formation in adult mice suggests a hitherto unrecognized role of VEGF in the maintenance of neuronal circuitry. Dysregulation of VEGF may lead to neurodegeneration through synaptic regression and dying-back axonopathy

Cellular Simultanous Recurrent Networks for Image Processing

Artificial neural networks are inspired by the abilities of humans and animals to learn and adapt. Feed-forward networks are both fast and powerful, and are particularly useful for statistical pattern recognition. These networks are inspired by portions of the brain such as the visual cortex. However, feed-forward networks have been shown inadequate for complex applications such as long-term optimization, reinforced learning and image processing. Cellular Neural Networks (CNNs) are a type of recurrent network which have been used extensively for image processing. CNNs have shown limited success solving problems which involve topological relationships. Such problems include geometric transformations such as affine transformation and image registration. The Cellular Simultaneous Recurrent Network (CSRN) has been exploited to solve the 2D maze traversal problem, which is a long-term optimization problem with similar topological relations. From its inception, it has been speculated that the CSRN may have important implications in image processing. However, to date, very little work has been done to study CSRNs for image processing tasks. In this work, we investigate CSRNs for image processing. We propose a novel, generalized architecture for the CSRN suitable for generic image processing tasks. This architecture includes the use of sub-image processing which greatly improves the efficacy of CSRNs for image processing. We demonstrate the application of the CSRN with this generalized architecture across a variety of image processing problems including pixel level transformations, filtering, and geometric transformations. Results are evaluated and compared with standard MATLAB® functions. To better understand the inner workings of the CSRN we investigate the use of various CSRN cores including: 1) the original Generalized Multi-Layered Perceptron (GMLP) core used by Pang and Werbos to solve the 2D maze traversal problem, 2) the Elman Simultaneous Recurrent Network (ESRN), and 3) a novel ESRN core with multi-layered feedback. We compare the functionality of these cores in image processing applications. Further, we introduce the application of the unscented Kalman filter (UKF) for training of the CSRN. Results are compared with the standard Extended Kalman Filter (EKF) training method of CSRN. Finally, implications of current findings and proposed research directions are presented

Molecular, Cellular and Mechanical basis of Epithelial Morphogenesis during Tribolium Embryogenesis

Embryonic development entails a series of morphogenetic events which require a precise coordination of molecular mechanisms coupled with cellular dynamics. Phyla such as arthropods show morphological and gene expression similarities during middle embryogenesis (at the phylotypic germband stage), yet early embryogenesis adopts diverse developmental strategies. In an effort towards understanding patterns of conservation and divergence during development, investigations are required beyond the traditional model systems. Therefore, in the past three decades, several insect species representing various insect orders have been established as experimental model systems for comparative developmental studies. Among these, the red flour beetle Tribolium castaneum has emerged as the best studied holometabolous insect model after the fruit fly Drosophila melanogaster. Unlike Drosophila, Tribolium is a short-germ insect that retains many ancestral characters common to most insects. The early embryogenesis of Tribolium shows dynamic epithelial rearrangements with an epibolic expansion of the extraembryonic tissue serosa over the embryo, the folding of the embryo in between the serosa and the second extra embryonic tissue amnion and the folding of the amnion underneath the embryo. These extensive tissues are evolutionarily conserved epithelia that undergo different tissue movements and are present in varying proportions in different insects, providing exceptional material to compare and contrast morphogenesis during early embryogenesis. However, most of the previous work on insects including Tribolium have largely focused on the conservation and divergence of gene expression patterns and on gene regulatory interactions. Consequently, very little studies on dynamic cell behaviour have been done and we lack detailed information about the cellular and tissue dynamics during these early morphogenetic events. During my PhD, I first established a live imaging and data analysis pipeline for studying Tribolium embryogenesis in 4-D. I combined live confocal and lightsheet imaging of transgenic or transiently labelled embryos with mechanical or genetic perturbations using laser ablations and gene knockdowns. Using this pipeline quantifications of cell dynamics and tissue behaviours can be done to compare different regions of the embryo as the development proceeds. In the second and third part of my thesis, I describe the actomyosin dynamics and associated cell behaviours during the stages of serosa epibolic expansion, amniotic fold formation and serosa window closure. I cloned and characterised the cellular dynamics of the Tribolium spaghetti squash gene (Tc-squash) - the non-muscle Myosin II regulatory light chain, which is the main molecular force generator in epithelial cells. Interestingly, the analysis of Tc-squash dynamics indicates a conserved role of Myosin II in controlling similar cell behaviours across short germ and long germ embryos. In the last part of the thesis, I report the dynamics of an actomyosin cable that emerges at the interface of the serosa and amnion. This cable increases in tension during development, concomitant with serosa tissue expansion and increased tensions in the serosa. It behaves as a modified purse string as it’s circumference shrinks due to a decrease in the number of cable forming cells over time. This shrinkage is an individual contractile property of the cells forming the cable. This indicates that a supracellular and contractile actomyosin cable might be functional during serosa window closure in insects with distinct serosa and amnion tissues. Further, the tension in the cable might depend on the relative proportion of the serosa, amnion and embryonic regions. Using these integrated approaches, I have correlated global cellular dynamics during early embryogenesis with actomyosin behaviours, and then performed a high-resolution analysis and perturbations of selected events. The established imaging, image processing and perturbation tools can serve as an important basis for future investigations into the tissue mechanics underlying Tribolium embryogenesis and can also be adapted for comparisons of morphogenesis in other insect embryos. More broadly, correlating the existing genetic, mechanical and biochemical understanding of developmental processes from Drosophila with species such as Tribolium, could help identify deeply conserved design principles that lead to different morphologies through differences in underlying regulation.:Page List of Tables v List of Figures vii 1 Introduction 1 1.1 Evo-Devo of insects 3 1.2 Tribolium castaneum 5 1.3 Fluorescence live imaging and lightsheet microscopy 10 1.4 Morphogenesis 15 1.5 Thesis objective 29 2 4D lightsheet imaging and analysis pipeline of Tribolium embryos 33 2.1 Standardisation of an injection protocol for sample mounting and imaging with the Zeiss LZ1 SPIM 35 2.2 Double labelling of Tribolium embryos 37 2.3 Image processing with Fiji 37 2.4 Long term timelapse imaging of Tribolium embryogenesis with SPIM 44 2.5 2D cartographic projections of 3D data as a method to visualise and analyse SPIM data 47 2.6 Summary 59 3 Cellular dynamics of the non muscle Myosin II regulatory light chain - Tc-Squash 61 3.1 Tc-Squash dynamics during Tribolium embryogenesis 64 3.2 Myosin drives basal cell closure during blastoderm cellularisation 66 3.3 Myosin shows planar polarity in the embryonic tissue 69 3.4 Myosin accumulation and apical constriction of putative germ cells at the posterior pole 71 3.5 Myosin pulses during apical constriction of mesoderm cells 74 3.6 Myosin accumulates at the extraembryonic-embryonic boundary to form a contractile supracellular cable 77 3.7 Summary 77 4 A supracellular actomyosin cable operates during serosa epiboly 79 4.1 Actin and Myosin accumulate at the extraembryonic-embryonic boundary 81 4.2 The actomyosin assembly migrates ventrally till it forms the rim of the serosa window 82 4.3 The actomyosin cable shows dynamic shape changes during serosa window closure 87 4.4 Serosa cells increase in area till circular serosa window stage 89 4.5 Tension in the serosa tissue increases during epibolic expansion 89 4.6 Serosa cells decrease their apical areas after laser ablation 92 4.7 Tension in the actomyosin cable increases during serosa epiboly 93 4.8 Myosin dynamics at the cable changes between early and serosa window stage 96 4.9 Individual cell membrane shrinkage and cell rearrangements decrease the cable circumference 98 4.10 Myosin dynamics at the cable during serosa window closure 101 4.11 Tension in the cable is not relieved after multiple laser cuts 103 4.12 Analysis of the actomyosin cable in Tc-zen 1 knockdown 105 4.13 Summary 109 5 Discussion 111 5.1 Reconstruction of insect embryogenesis using lightsheet microscopy and tissue cartography 111 5.2 Conserved Myosin II behaviours and its implications on morphogenesis across insects 114 5.3 A contractile supracellular actomyosin cable functions serosa window closure in Tribolium 119 6 Materials and Methods 123 6.1 Tribolium stock maintenance 123 6.2 RNA extraction and cDNA synthesis 124 6.3 Cloning of templates for mRNA synthesis and transgenesis 124 6.4 dsRNA synthesis for RNAi experiments 126 6.5 Capped, single stranded RNA synthesis 126 6.6 Fluorescence image acquisition 27 A Appendix 131 Bibliography 14

Doctor of Philosophy

dissertationThe development of an animal from a single cell to an organism requires that individual cells undergo several sequential processes. The final stages, termed commitment and differentiation, rely heavily on the cell-intrinsic activity of regulatory transcription factor networks. The early B cell factor (EBF) family transcription factors are known to have an important influence on commitment and differentiation in neurons, B cells and adipocytes in vertebrate animals, and muscle cells in invertebrate animals. The full range of their activity, though, is not understood. We have utilized a microarray screen in Xenopus laevis to identify an extensive list of candidate targets of EBF transcriptional activity, as a step toward expanding understanding of the scope of EBF functions. This thesis focuses on the functions of EBF proteins in neuron and muscle cell development. To expand current knowledge of EBF functions in neuronal development, we selectively chose candidate targets from the microarray screen that have expected function in neurons, and verified that their expression depends on EBF activity. These targets demonstrate several previously unknown functions of EBF proteins in neuronal cell commitment and differentiation. We also have discovered a new function of the EBF protein partner ZFP423 as a synergistic mediator of the critical Notch signaling pathway, and show that EBF proteins can promote neuronal commitment in part by blocking the function of ZFP423. We next demonstrate that EBF proteins are necessary for normal Xenopus skeletal muscle development, and that they act by controlling expression of several genes critical for commitment and differentiation of muscle cells. This thesis significantly contributes to understanding of the function of EBF proteins in neuronal development. It also demonstrates for the first time an important role for EBF proteins in muscle cell development in vertebrates. Overall, this thesis expands our understanding of how EBF proteins participate in the complex transcriptional regulation of vertebrate development